1. Technical Field
This application generally relates to neural networks, and more particularly to verification of neural networks.
2. Description of Related Art
Trained neural networks may be verified using a variety of different approaches. One approach is a deterministic approach in which the neural network is verified by exhaustive testing. The neural network is verified at testing points based on the discretization of a search space representing the set of neural network inputs or underlying independent state variables upon which the neural network inputs may be based. The size of the search space or number of test points increases with the dimension of the search space. As the dimensionality increases, neural networks may not be practically verified using this approach with existing computer systems. The currently available deterministic method allows for verification of neural networks but suffers from the curse of dimensionality as just described.
Another approach that may be used in verification of neural networks is a randomized approach. Using this technique, a reduced number of sample points may be used to verify the performance of a neural network within an acceptable error limit. However, even with the randomized approach, the number of sample points required for verification purposes may be impractical as the threshold of acceptable error approaches zero.
Thus, it may be desirable to have an efficient method for verifying a neural network or other component that may be used with a low acceptable error threshold. It may be desirable that this technique also be usable with higher problem space or search space dimensions. It may also be desirable that this technique be independent of the problem space dimension.